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 * Copyright 2011-2022 Jose Luis Blanco (joseluisblancoc@gmail.com).
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#include <cstdlib>
#include <ctime>
#include <iostream>
#include <nanoflann.hpp>

using namespace std;
using namespace nanoflann;

// This is an exampleof a custom data set class
template <typename T>
struct PointCloud
{
    struct Point
    {
        T x, y, z;
    };

    std::vector<Point> pts;

    // Must return the number of data points
    inline size_t kdtree_get_point_count() const { return pts.size(); }

    // Returns the dim'th component of the idx'th point in the class:
    // Since this is inlined and the "dim" argument is typically an immediate
    // value, the
    //  "if/else's" are actually solved at compile time.
    inline T kdtree_get_pt(const size_t idx, const size_t dim) const
    {
        if (dim == 0)
            return pts[idx].x;
        else if (dim == 1)
            return pts[idx].y;
        else
            return pts[idx].z;
    }

    // Optional bounding-box computation: return false to default to a standard
    // bbox computation loop.
    //   Return true if the BBOX was already computed by the class and returned
    //   in "bb" so it can be avoided to redo it again. Look at bb.size() to
    //   find out the expected dimensionality (e.g. 2 or 3 for point clouds)
    template <class BBOX>
    bool kdtree_get_bbox(BBOX& /* bb */) const
    {
        return false;
    }
};

template <typename T>
PointCloud<T> generateRandomPointCloud(const size_t N, const T max_range = 10)
{
    PointCloud<T> point;
    point.pts.resize(N);
    for (size_t i = 0; i < N; i++)
    {
        point.pts[i].x = max_range * (rand() % 1000) / T(1000);
        point.pts[i].y = max_range * (rand() % 1000) / T(1000);
        point.pts[i].z = max_range * (rand() % 1000) / T(1000);
    }
    return point;
}

template <typename num_t>
void kdtree_demo(const size_t N, double& buildTimer, double& queryTimer)
{
    PointCloud<num_t> cloudS = generateRandomPointCloud<num_t>(N);
    PointCloud<num_t> cloudT = generateRandomPointCloud<num_t>(N);

    clock_t begin = clock();
    // construct a kd-tree index:
    using my_kd_tree_t = KDTreeSingleIndexAdaptor<
        L2_Simple_Adaptor<num_t, PointCloud<num_t>>, PointCloud<num_t>,
        3 /* dim */
        >;

    my_kd_tree_t index(3 /*dim*/, cloudS, {10 /* max leaf */});
    index.buildIndex();
    clock_t end = clock();
    buildTimer += double(end - begin) / CLOCKS_PER_SEC;

    {
        double elapsed_secs = 0;
        for (size_t i = 0; i < N; i++)
        {
            num_t query_pt[3] = {
                cloudT.pts[i].x, cloudT.pts[i].y, cloudT.pts[i].z};
            // do a knn search
            const size_t                   num_results = 1;
            size_t                         ret_index;
            num_t                          out_dist_sqr;
            clock_t                        begin = clock();
            nanoflann::KNNResultSet<num_t> resultSet(num_results);
            resultSet.init(&ret_index, &out_dist_sqr);
            index.findNeighbors(
                resultSet, query_pt, nanoflann::SearchParams(10));
            clock_t end = clock();
            elapsed_secs += double(end - begin);
        }
        elapsed_secs = elapsed_secs / CLOCKS_PER_SEC;
        queryTimer += elapsed_secs / N;
    }
}

int main(int argc, char* argv[])
{
    size_t plotCount = 10;
    size_t maxSize   = 10000;

    if (argc == 3)
    {
        srand(atoi(argv[2]));
        maxSize = atoi(argv[1]);
    }
    else
    {
        cerr
            << "**Running Instructions:**\n ./benchmark_nanoflann_random "
               "numPoints seed\nExample:\n ./benchmark_nanoflann_random 10000 1"
            << endl;
        return 0;
    }

    // buildTime : time required to build the kd-tree index
    // queryTime : time required to find nearest neighbor for a single point in
    // the kd-tree
    vector<double> buildTime, queryTime;

    for (size_t i = 1; i <= plotCount; i++)
    {
        size_t currSize = ((i * 1.0) / plotCount) * maxSize;
        std::cout << currSize << " ";
        double buildTimer = 0, queryTimer = 0;
        kdtree_demo<float>(currSize, buildTimer, queryTimer);
        buildTime.push_back(buildTimer);
        queryTime.push_back(queryTimer);
    }
    cout << "\n";
    for (size_t i = 0; i < buildTime.size(); i++)
        std::cout << buildTime[i] << " ";
    std::cout << "\n";

    for (size_t i = 0; i < queryTime.size(); i++)
        std::cout << queryTime[i] << " ";
    std::cout << "\n";
    return 0;
}
